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Overview of Verticals| Hitchhikers Platform
What You’ll Learn
In this section, you will learn:
- Review of universal vs. vertical search
- Common examples of verticals
- Intro to searchable fields
Let’s Review: Universal vs. Vertical Search
There are two types of search that you’ll become very familiar with in Answers:
- Universal Search - search across multiple verticals
- Vertical Search - search across a single vertical
As a reminder, when we refer to a Vertical we’re talking about a class of results. This is often synonymous with an Entity Type (though you can choose to have multiple entity types in a given vertical). When you build an Answers experience, you’re mostly building out and defining each vertical — what it should look like, what entity types and fields should be considered, any boosts or blacklisted intents, how you want results sorted, and so on.
Popular Examples of Verticals
When you are defining your Verticals, you’ll want to think in terms of intent. The Yext Answers Experience, for example, has several key intents that it wants a user to convert on —
- Scheduling a demo
- Applying for a job
- Downloading a whitepaper
These three example intents would produce three verticals, respectively — a Products Vertical for demo intents, a Jobs Vertical for application intents, and a Resources Vertical for the whitepaper-download intents! In the Knowledge Graph, we’d load Products, Jobs, and Resources as separate entity types.
Additional examples of popular Verticals include:
- Case Studies
And so many more…
Take a look at some of the verticals Yext has in its main Answers Experience: Products, Jobs, Solutions, Webinars, Events, and more. These were all developed while thinking strategically about key intents for Yext and how it drives leads, revenue, and business opportunities:
Intro to Searchable Fields
Within each vertical, you will determine which fields can be searched on the Answers backend. This is what’s called a searchable field.
Searchable fields control which fields are indexed by the Answers algorithm in each vertical and therefore how results (think: entities and verticals) are returned to the user. Each entity type that you set up in your Knowledge Graph has both profile and custom fields associated to it. In the Search Configuration, you can set each field as Searchable by configuring it as such and selecting one of the following searchable field types in the below table. We will dive into each type and walk through examples of each one in the following units!
|Searchable Field||Description||Common Use Cases (Fields)|
|Text Search||Text Search allows each token of the query to be searched within the fields you specify. This is best for short text fields that might contain unstructured or varied data.||Name, Keywords|
|NLP Filter||NLP Filter enables each term in the search query to be parsed with Natural Language Processing (NLP) and used as a filter. This is best for structured enum/option fields where there are a finite number of variations across entities.||builtin.location, builtin.entityType, linkedEntity.name, Department, Category, Vertical, Industry|
|Facet||Facets allow a field to be used as a type of dynamic filter that a user can interact with in the search experience to narrow their search. This is best for structured enum/option fields where there are a finite number of variations across entities.||Department, Category, Vertical, Industry, Department, Insurance, Services, Payment Methods|
|Sortable||Sortable allows a field to be used as a sorting method that is controlled by the algorithm or by the user.||Name, Date, Popularity, Accepting New Patients|
|Direct Answer||Direct Answer allows a field to be surfaced in a prioritized direct answer card within the search experience.||Address, phone, title, calories, allergens|
|Phrase Match||Phrase Match allows an entity to be surfaced only when there is an exact phrase match contained in the query.||Keywords, First Name, Last Name|
|Semantic Text Search||This turns on our Semantic Text Search algorithm, which will match a user’s query to an entity name that is semantically similar (not available for Location entities).||Name, especially FAQ Name and Help Article Name|
|Document Search||This turns on our Document Search algorithm, which searches long, unstructured content from a Knowledge Graph entity and returns featured snippets.||Description and Body fields from Help Articles, Blogs, Product Descriptions, and much more|
Throughout this module, we will break down each searchable field type and provide examples use cases and best practices for each one.
Intro to Direct Answers
In this module you will also learn how to configure Direct Answers. Both types of Direct Answers will surface from the fields specified by you, the Admin, within a Search Configuration. Direct Answers can be set up in either a field format or featured snippet format.
Field Value Direct Answers (surfacing from structured Knowledge Graph data)
Featured Snippet Direct Answers (surfacing from unstructured Knowledge Graph data)
You will learn how to set up both types of Direct Answers towards the end of this module!